Deep affect prediction in-the-wild: Aff-wild database and challenge, deep architectures, and beyond

Kollias, Dimitrios, Tzirakis, Panagiotis, Nicolaou, Mihalis A., Papaioannou, Athanasios, Zhao, Guoying, Schuller, Björn, Kotsia, Irene and Zafeiriou, Stefanos (2019) Deep affect prediction in-the-wild: Aff-wild database and challenge, deep architectures, and beyond. International Journal of Computer Vision . pp. 1-23. ISSN 1573-1405 (Published online first)

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Abstract

Automatic understanding of human affect using visual signals is of great importance in everyday human–machine interac- tions. Appraising human emotional states, behaviors and reactions displayed in real-world settings, can be accomplished using latent continuous dimensions (e.g., the circumplex model of affect). Valence (i.e., how positive or negative is an emo- tion) and arousal (i.e., power of the activation of the emotion) constitute popular and effective representations for affect. Nevertheless, the majority of collected datasets this far, although containing naturalistic emotional states, have been captured in highly controlled recording conditions. In this paper, we introduce the Aff-Wild benchmark for training and evaluating affect recognition algorithms. We also report on the results of the First Affect-in-the-wild Challenge (Aff-Wild Challenge) that was recently organized in conjunction with CVPR 2017 on the Aff-Wild database, and was the first ever challenge on the estimation of valence and arousal in-the-wild. Furthermore, we design and extensively train an end-to-end deep neural architecture which performs prediction of continuous emotion dimensions based on visual cues. The proposed deep learning architecture, AffWildNet, includes convolutional and recurrent neural network layers, exploiting the invariant properties of convolutional features, while also modeling temporal dynamics that arise in human behavior via the recurrent layers. The AffWildNet produced state-of-the-art results on the Aff-Wild Challenge. We then exploit the AffWild database for learning features, which can be used as priors for achieving best performances both for dimensional, as well as categorical emo- tion recognition, using the RECOLA, AFEW-VA and EmotiW 2017 datasets, compared to all other methods designed for the same goal. The database and emotion recognition models are available at http://ibug.doc.ic.ac.uk/resources/first-affect-wild-challenge.

Item Type: Article
Research Areas: A. > School of Science and Technology > Computer Science
Item ID: 26522
Notes on copyright: © The Author(s) 2019. Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
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Depositing User: Irene Kotsia
Date Deposited: 01 May 2019 15:02
Last Modified: 02 May 2019 09:28
URI: https://eprints.mdx.ac.uk/id/eprint/26522

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